This study introduces HQ-Edit, a high-quality instruction-based image editing dataset with around 200,000 edits. Unlike prior approaches relying on attribute guidance or human feedback on building datasets, we devise a scalable data collection pipeline leveraging advanced foundation models, namely GPT-4V and DALL-E 3. To ensure its high quality, diverse examples are first collected online, expanded, and then used to create high-quality diptychs featuring input and output images with detailed text prompts, followed by precise alignment ensured through post-processing. In addition, we propose two evaluation metrics, Alignment and Coherence, to quantitatively assess the quality of image edit pairs using GPT-4V. HQ-Edits high-resolution images, rich in detail and accompanied by comprehensive editing prompts, substantially enhance the capabilities of existing image editing models. For example, an HQ-Edit finetuned InstructPix2Pix can attain state-of-the-art image editing performance, even surpassing those models fine-tuned with human-annotated data.

Enhancing the robustness of vision algorithms in real-world scenarios is challenging. One reason is that existing robustness benchmarks are limited, as they either rely on synthetic data or ignore the effects of individual nuisance factors. We introduce OOD-CV-v2, a benchmark dataset that includes out-of-distribution examples of 10 object categories in terms of pose, shape, texture, context and the weather conditions, and enables benchmarking of models for image classification, object detection, and 3D pose estimation. In addition to this novel dataset, we contribute extensive experiments using popular baseline methods, which reveal that: 1) Some nuisance factors have a much stronger negative effect on the performance compared to others, also depending on the vision task. 2) Current approaches to enhance robustness have only marginal effects, and can even reduce robustness. 3) We do not observe significant differences between convolutional and transformer architectures. We believe our dataset provides a rich test bed to study robustness and will help push forward research in this area.

This paper targets the problem of multi-task dense prediction which aims to achieve simultaneous learning and inference on a bunch of multiple dense prediction tasks in a single framework. A core objective in design is how to effectively model cross-task interactions to achieve a comprehensive improvement on different tasks based on their inherent complementarity and consistency. Existing works typically design extra expensive distillation modules to perform explicit interaction computations among different task-specific features in both training and inference, bringing difficulty in adaptation for different task sets, and reducing efficiency due to clearly increased size of multi-task models. In contrast, we introduce feature-wise contrastive consistency into modeling the cross-task interactions for multi-task dense prediction. We propose a novel multi-task contrastive regularization method based on the consistency to effectively boost the representation learning of the different sub-tasks, which can also be easily generalized to different multi-task dense prediction frameworks, and costs no additional computation in the inference. Extensive experiments on two challenging datasets (i.e. NYUD-v2 and Pascal-Context) clearly demonstrate the superiority of the proposed multi-task contrastive learning approach for dense predictions, establishing new state-of-the-art performances.

With the development of Information and Communication Technology, the traditional industrial organization cannot satisfy the requirement of industrial development and on the contrary, Industrial Internet, as a new industrial infrastructure and organization form, is getting people's attention. Supported by the information platform, Industrial Internet pushed forward digitization, networking and intelligence upgrading of the industry, and, it realized the industrial chain collaboration, intensive development, value cocreation and co-governance of ecosystem. This paper researched the current situation of the garment and textile industry, analyzed the universal requirements of industrial internet for data, collaboration and architecture, and made the business mode and platform operating mechanism of industrial internet. Then the architecture and key components of industrial internet platform were planned on the base of the cloud native. Finally, the service system and demonstration project of the industrial internet platform were proposed. The paper has the reference function for the creation and operation of the garment and textile industry internet platform.

We present an intensity-based nonrigid registration approach for the normalization of 3-D multichannel microscopy images of cell nuclei. A main problem with cell nuclei images is that the intensity structure of different nuclei differs very much; thus, an intensity-based registration scheme cannot be used directly. Instead, we first perform a segmentation of the images from the cell nucleus channel, smooth the resulting images by a Gaussian filter, and then apply an intensity-based registration algorithm. The obtained transformation is applied to the images from the nucleus channel as well as to the images from the other channels. To improve the convergence rate of the algorithm, we propose an adaptive step length optimization scheme and also employ a multiresolution scheme. Our approach has been successfully applied using 2-D cell-like synthetic images, 3-D phantom images as well as 3-D multichannel microscopy images representing different chromosome territories and gene regions. We also describe an extension of our approach, which is applied for the registration of 3D + t (4-D) image series of moving cell nuclei.